Why NLP Needs High-Volume, Human-Verified Training Data

Interpretation of natural language is not only complex and nuanced, but can vary widely based on context, frames of reference, individual preferences, and situational constraints, to name a few. While modern machine learning models can now predict and generate text without human input, they aren’t able to achieve sophisticated tasks like sentiment analysis, intent recognition, or document classification at human-level performance without large volumes of human-labeled data. The Alegion platform integrates the best of human and machine intelligence to deliver quality training data at scale, enabling enterprises to speed time to value and broaden their NLP applications.

Hands-Off Training Data Labeling

Reclaim valuable data scientist hours with training data that’s custom-labeled for your project—without having to label any of it.

The Human Neural Network

Model Accuracy with Quality Data

Accelerate development cycles and get value out of our your NLP model more quickly with accelerated model training.

How we advance your NLP model development

Whether it’s audio, transcription, hand-written text, or open datasets—we help you extract value out of your data quickly and at scale. Our platform and team take care of configuring the data labeling tasks and deploy them to a team of human specialists, who we train to meet your project and accuracy requirements. You get high-accuracy training data in a fraction of the time it would take to build it in-house.

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Hear from our customers

“Alegion gave us the scale to process thousands of social messages every day. Trying to do this internally or with an outsourced crowd wouldn’t have been logistically or financially feasible.”

–Trey Robinson, SVP Marketing, Charles Schwab

Enterprise-grade model training data is just a conversation away

Our team has built best practices servicing our Fortune 500 customers, so that we’re best equipped to enrich your training data for machine learning model development. Partner with us to make our platform work for you, not the other way around.